Programmer : Python + SQL Developer (Backend + Data Analytics) Role: We are looking for a versatile Python + SQL Developer who can work across both backend application development and data analytics workflows . You will build scalable APIs, automate data pipelines, and enable data-driven decision-making through analytical scripting and reporting. Key Responsibilities Develop and maintain backend systems and REST APIs using Python (Flask, FastAPI, or Django). Design and optimize relational databases (e.g., PostgreSQL, MySQL) for both application logic and analytics. Build and automate ETL workflows for internal data processing and reporting. Write complex SQL queries for data extraction, transformation, and aggregation. Generate analytical outputs, dashboards, and insights using Python (Pandas, Matplotlib/Plotly). Collaborate with cross-functional teams (Product, Ops, Data) to deliver data-backed features and reports. Expert in Pivot Tables in Excel Desired Candidate Profile Required Skills Proficiency in Python for backend and scripting tasks. Strong expertise in SQL and relational databases . Experience building APIs and backend services using Flask , FastAPI , or Django . Hands-on with Pandas , NumPy , and other analytics libraries. Hands on Pivot Table Ability to write reusable, well-structured code with good documentation. Experience with version control tools like Git . Job Benefits & Perks As per General IT industry Practices
Passion for applying AI and data-driven thinking to improve processes, enhance decision-making, and drive innovation. Role: Data Collection : Gather data from various sources, including databases, spreadsheets, and external datasets. Data Cleaning : Ensure data quality by identifying and correcting errors or inconsistencies in the data. Data Analysis : Analyze data using statistical methods to uncover trends, patterns, and insights. Reporting : Create reports and visualizations to present findings to stakeholders in a clear and actionable manner. Tool Development : Develop and maintain dashboards or tools that facilitate ongoing data analysis and reporting.. Responsibilities: 1.Data Management : Maintain databases and ensure data integrity. Develop data models to support analysis. 2.Statistical Analysis : Use statistical techniques to analyze data sets. 3.Visualization : Create visualizations (charts, graphs, etc.) to communicate findings effectively. Use tools like Looker for reporting. 4.Business Insights : Interpret data to provide actionable insights to improve business performance. Identify trends and make recommendations based on analysis. 5 Documentation : Document processes, methodologies, and findings to ensure transparency and reproducibility. Prepare user guides or training materials for tools developed. 6.Continuous Improvement : Stay updated on industry trends and best practices in data analysis. Seek opportunities to improve processes and tools for more effective data analysis. Desired Candidate Profile 3+ years of work experience in data management disciplines including data integration, modeling, optimization and data quality, and/or other areas directly relevant to data Analytics responsibilities and tasks. Experience working in cross-functional teams and collaborating with business stakeholders in support of a departmental and/or multi-departmental data management and analytics initiative. A bachelor's or master's degree in computer science, statistics, applied mathematics, data management, information systems, information science or a related quantitative field or equivalent work experience is required. Experience in Fintech and/or Insurance industry will be a preferred Job Benefits & Perks As per General IT industry Practices
Passion for applying AI and data-driven thinking to improve processes, enhance decision-making, and drive innovation. Role: Data Collection : Gather data from various sources, including databases, spreadsheets, and external datasets. Data Cleaning : Ensure data quality by identifying and correcting errors or inconsistencies in the data. Data Analysis : Analyze data using statistical methods to uncover trends, patterns, and insights. Reporting : Create reports and visualizations to present findings to stakeholders in a clear and actionable manner. Tool Development : Develop and maintain dashboards or tools that facilitate ongoing data analysis and reporting.. Responsibilities: 1.Data Management : Maintain databases and ensure data integrity. Develop data models to support analysis. 2.Statistical Analysis : Use statistical techniques to analyze data sets. 3.Visualization : Create visualizations (charts, graphs, etc.) to communicate findings effectively. Use tools like Looker for reporting. 4.Business Insights : Interpret data to provide actionable insights to improve business performance. Identify trends and make recommendations based on analysis. 5 Documentation : Document processes, methodologies, and findings to ensure transparency and reproducibility. Prepare user guides or training materials for tools developed. 6.Continuous Improvement : Stay updated on industry trends and best practices in data analysis. Seek opportunities to improve processes and tools for more effective data analysis. Desired Candidate Profile 3+ years of work experience in data management disciplines including data integration, modeling, optimization and data quality, and/or other areas directly relevant to data Analytics responsibilities and tasks. Experience working in cross-functional teams and collaborating with business stakeholders in support of a departmental and/or multi-departmental data management and analytics initiative. A bachelor's or master's degree in computer science, statistics, applied mathematics, data management, information systems, information science or a related quantitative field or equivalent work experience is required. Experience in Fintech and/or Insurance industry will be a preferred Job Benefits & Perks As per General IT industry Practices
Role & responsibilities We are seeking a skilled Cybersecurity Engineer / Specialist with strong expertise in Google Cloud Platform (GCP) security and cloud management . The role involves securing cloud infrastructure, monitoring threats, ensuring compliance, and building automation to protect mission-critical systems. You will collaborate with DevOps and engineering teams to design and implement best-in-class cloud security practices. Responsibilities: Design, implement, and manage secure GCP workloads including IAM, VPCs, firewall policies, and encryption. Deploy and maintain Google Security Command Center, Chronicle SIEM, Cloud Armor, and KMS . Monitor and investigate security incidents, vulnerabilities, and anomalies in cloud environments. Conduct risk assessments, compliance checks, and audits (ISO 27001, SOC2, GDPR, HIPAA). Automate cloud security processes using Terraform, Python, and CI/CD integrations . Partner with DevOps and Infrastructure teams to embed DevSecOps best practices . Preferred candidate profile 3 / 7 years of experience in cybersecurity with at least 2 years on GCP security . Strong knowledge of IAM, SCC, Chronicle, DLP, Cloud Armor, KMS, and VPC security . Hands-on with security automation and scripting (Python, Bash, Terraform). Experience in incident response, vulnerability management, and SIEM/SOAR tools . Familiar with regulatory frameworks and compliance requirements. Certifications preferred: Google Professional Cloud Security Engineer, CISSP, CEH, or CISM .
Role & responsibilities We are seeking a skilled Google cloud Developer / Specialist with strong expertise in Google Cloud Platform (GCP) security and cloud management . The role involves securing cloud infrastructure, monitoring threats, ensuring compliance, and building automation to protect mission-critical systems. You will collaborate with DevOps and engineering teams to design and implement best-in-class cloud security practices. Responsibilities: Design, implement, and manage secure GCP workloads including IAM, VPCs, firewall policies, and encryption. Deploy and maintain Google Security Command Center, Chronicle SIEM, Cloud Armor, and KMS . Monitor and investigate security incidents, vulnerabilities, and anomalies in cloud environments. Conduct risk assessments, compliance checks, and audits (ISO 27001, SOC2, GDPR, HIPAA). Automate cloud security processes using Terraform, Python, and CI/CD integrations . Partner with DevOps and Infrastructure teams to embed DevSecOps best practices . Preferred candidate profile 3 / 7 years of experience in cybersecurity with at least 2 years on GCP security . Strong knowledge of IAM, SCC, Chronicle, DLP, Cloud Armor, KMS, and VPC security . Hands-on with security automation and scripting (Python, Bash, Terraform). Experience in incident response, vulnerability management, and SIEM/SOAR tools . Familiar with regulatory frameworks and compliance requirements. Certifications preferred: Google Professional Cloud Security Engineer, CISSP, CEH, or CISM .
Passion for applying AI and data-driven thinking to improve processes, enhance decision-making, and drive innovation. Role: Data Collection : Gather data from various sources, including databases, spreadsheets, and external datasets. Data Cleaning : Ensure data quality by identifying and correcting errors or inconsistencies in the data. Data Analysis : Analyze data using statistical methods to uncover trends, patterns, and insights. Reporting : Create reports and visualizations to present findings to stakeholders in a clear and actionable manner. Tool Development : Develop and maintain dashboards or tools that facilitate ongoing data analysis and reporting.. Responsibilities: 1.Data Management : Maintain databases and ensure data integrity. Develop data models to support analysis. 2.Statistical Analysis : Use statistical techniques to analyze data sets. 3.Visualization : Create visualizations (charts, graphs, etc.) to communicate findings effectively. Use tools like Looker for reporting. 4.Business Insights : Interpret data to provide actionable insights to improve business performance. Identify trends and make recommendations based on analysis. 5 Documentation : Document processes, methodologies, and findings to ensure transparency and reproducibility. Prepare user guides or training materials for tools developed. 6.Continuous Improvement : Stay updated on industry trends and best practices in data analysis. Seek opportunities to improve processes and tools for more effective data analysis. Desired Candidate Profile 6+ years of work experience in data management disciplines including data integration, modeling, optimization and data quality, and/or other areas directly relevant to data Analytics responsibilities and tasks. Experience working in cross-functional teams and collaborating with business stakeholders in support of a departmental and/or multi-departmental data management and analytics initiative. A bachelor's or master's degree in computer science, statistics, applied mathematics, data management, information systems, information science or a related quantitative field or equivalent work experience is required. Experience in Fintech and/or Insurance industry will be a preferred Job Benefits & Perks As per General IT industry Practices
Why Hudson Data Work on real data problems across fintech, lending, and AI-ops platforms. Exposure to full-stack analytics: modeling, automation, and product integration. Flat structure, high ownership, and visible impact on business outcomes. Continuous learning and mentorship from experienced data and product leaders. Role & responsibilities Hudson Data is a fast-growing analytics and AI company helping financial and technology clients turn raw operational data into predictive insights. We combine domain expertise, modern data infrastructure, and applied machine learning to solve high-value business problems in credit, collections, risk, and customer engagement. Were looking for a Data Scientist who thrives on execution someone who can own end-to-end analytics assignments, from data extraction to deployment, and deliver measurable impact to business outcomes. Responsibilities: As a Data Scientist at Hudson Data, you will build, validate, and deploy data-driven models and analytical solutions that directly improve operational KPIs such as repayment rates, lead conversion, risk segmentation, and campaign efficiency. Youll work closely with cross-functional teams in product, engineering, and operations to turn analytical insights into scalable, production-ready tools.Design and execute end-to-end data science projects from problem framing to deployment. Collect, clean, and integrate data from multiple systems (SQL, Excel, APIs, Snowflake, AWS). Build and optimize predictive models Validate and monitor model performance on live data; retrain as needed. Automate recurring analytical workflows using Python, SQL, and other data pipelines. Present actionable insights through dashboards and concise summaries for business teams. Partner with engineering to productionize models via APIs or embedded scoring scripts. Document datasets, code, and methodologies for transparency and reproducibility. Preferred candidate profile 5 to 7 years of hands-on experience in applied data science or analytics. Strong programming skills in Python (pandas, scikit-learn, NumPy) and SQL. Proven experience with predictive modeling, regression, classification, or clustering. Familiarity with cloud platforms (AWS / GCP / Azure) and version control (Git). Strong understanding of feature engineering, data validation, and model evaluation. Excellent communication skills and ability to translate analytical output into business impact.
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